Investment shocks and the relative price of investment

Justiniano, Alejandro; Primiceri, Giorgio E.; Tambalotti, Andrea

doi:10.1016/j.red.2010.08.004

Cited by 418 publications

(414 citation statements)

References 33 publications

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“…For example, a first order condition similar to (1) is ubiquitous in such models; even when (1) is generalized to allow features such as habit persistence there is still a link between trend growth and low frequency movement in r (see Hamilton et al (2016)). Some such models include technology shocks that lead to a trend in the relative price of investment (e.g., Justiniano et al (2011)). On the other hand, such models typically do not have a life cycle component, nor, so far as we know, do they tie secular movements in real rates to movements in inflation or money.…”

Section: 4mentioning

confidence: 99%

Some Evidence on Secular Drivers of US Safe Real Rates

Lunsford

West²

2017

Working Paper (Federal Reserve Bank of Cleveland)

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We study long-run correlations between safe real interest rates in the United States and over 20 variables that have been hypothesized to infl uence real rates. The list of variables is motivated by the familiar intertermporal IS equation, by models of aggregate savings and investment, and by reduced form studies. We use annual data, mostly from 1890 to 2016. We fi nd that safe real interest rates are correlated as expected with demographic measures. For example, the longrun correlation with labor force hours growth is positive, which is consistent with overlapping generations models. For another example, the long-run correlation with the proportion of 40-to 64-year-olds in the population is negative. This is consistent with standard theory where middle-aged workers are high-savers who drive down real interest rates. In contrast to standard theory, we do not fi nd productivity to be positively correlated with real rates. Most other variables have a mixed relationship with the real rate, with long-run correlations that are statistically or economically large in some samples and by some measures but not in others.

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Section: 4mentioning

confidence: 99%

Some Evidence on Secular Drivers of US Safe Real Rates

Lunsford

West²

2017

Working Paper (Federal Reserve Bank of Cleveland)

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“…Due to non-availability of Producer Price Index data in India, following Mohanty (2010) The IST shock is backed out from our estimated DSGE model in a similar spirit as in Justiniano, et al (2011). The model performs well in predicting the signs of all key correlations except a few correlations involving relative price of investment and employment.…”

Section: Matching Business Cycle Factsmentioning

confidence: 99%

“…Other disturbances can play crucial roles for business cycles (Justiniano et al, 2011). Gali (1999 summarizes that the TFP shock accounts for roughly 5% and 7% of the fluctuations of labour hours and output in the US during the post war period.…”

Section: Introductionmentioning

confidence: 99%

Technology Shocks and Business Cycles in India

Banerjee

Basu

2017

Macroecon. Dynam.

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractIn this paper, we develop a small open economy New Keynesian DSGE model to understand the relative importance of two key technology shocks, Hicks neutral total factor productivity (TFP) shock and investment specific technology (IST) shock for an emerging market economy like India. In addition to these two shocks, our model includes three demand side shocks such as fiscal spending, home interest rate, and foreign interest rate. Using a Bayesian approach, we estimate our DSGE model with Indian annual data for key macroeconomic variables over the period of 1971-2010, and for sub-samples of pre-liberalization (1971-1990) and post-liberalization (1991-2010) periods. Our study reveals three main results. First, output correlates positively with TFP, but negatively with IST. Second, TFP and IST shocks are the first and the second most important contributors to aggregate fluctuations in India. In contrast, the demand side disturbances play a limited role. Third, although TFP plays a major role in determining aggregate fluctuations, its importance vis-à-vis IST has declined during the post liberalization era. We find that structural shifts of nominal friction and relative home bias for consumption to investment in the post-liberalization period can account for the rising importance of the IST shocks in India.

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“…Likewise, investment shocks affect the economy's ability to transform consumption goods into productive capital and thus play a parallel role to the process of financial intermediation. Justiniano et al (2011), for example, draw an explicit link between shocks to the marginal efficiency of investment and credit risk spreads. Credit spreads imply the existence of a material financial friction, yet the model in Justiniano et al (2011) has no such friction.…”

Section: Introductionmentioning

confidence: 99%

“…Justiniano et al (2011), for example, draw an explicit link between shocks to the marginal efficiency of investment and credit risk spreads. Credit spreads imply the existence of a material financial friction, yet the model in Justiniano et al (2011) has no such friction.…”

Section: Introductionmentioning

confidence: 99%

Financial Frictions and the Role of Investment Specific Technology Shocks in the Business Cycle

2012

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Shocks affecting the rate at which investment goods are transformed into capital stock have been identified as a major driver of the business cycle. Such shocks have been linked to frictions in financial markets, because financial markets are instrumental in transforming consumption goods into installed capital. Yet we show that the importance of these investment shocks is greatly diminished when collateral constraints on firms are introduced into an estimated dynamic stochastic general equilibrium model. In the presence of binding collateral constraints, risk premium shocks take on a more prominent role as drivers of the business cycle. Modellers of business cycle fluctuations need to be mindful of the incompatibility of investment shocks and collateral constraints and of the difficulty in specifying 'structural' shocks that are robust to modest amendments to the frictions present in a model.

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Investment shocks and the relative price of investment

Cited by 418 publications

References 33 publications

Some Evidence on Secular Drivers of US Safe Real Rates

Some Evidence on Secular Drivers of US Safe Real Rates

Technology Shocks and Business Cycles in India

Financial Frictions and the Role of Investment Specific Technology Shocks in the Business Cycle

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